1. Technical Field
The present invention relates to an abnormality detection system that detects abnormality in control characteristic values of a plurality of products manufactured on the same production line, and to a method of detecting the abnormality.
2. Related Art
Administration process based on control charts currently in practice has been established on the premise of employing data acquired through measurement, calculating averages and ranges by hand, and marking plots on graphs. Thus, various contrivances that can eliminate the need to use a calculator are incorporated in the process.
Japanese Laid-open patent publication No. H05-333834 (patent document 1) discloses a control chart display apparatus that distinctly displays on the same screen a plurality of abnormal points in the control chart. The examples of the abnormalities to be detected cited in this document are following (a) to (e).
(a) Maldistribution on one side. The case where a majority of the points concentrates on one side of the center. For example, when 7 consecutive points all, 10 points or more out of 11 consecutive points, 12 points or more out of 14 consecutive points, 14 points or more out of 17 consecutive points, or 16 points or more out of 20 consecutive points appear on one side of the center.
(b) Upward trend or downward trend
(c) Periodicity
(d) Approximation to a control limit
(e) Excess of the control limit
FIG. 5 is a graph included in the patent document 1 as an example of the display of the abnormal points. In the graph, the center line CL (center line), the upper control limit UCL (upper control limit), and the lower control limit LCL (lower control limit) are shown. Here, a plot 131 and a plot 132 indicate the abnormality. The plot 131 corresponds to the item (a), because the consecutively 7 points including the plot 131 are below the center line CL. The plot 132 corresponds to the item (e), because of exceeding the upper control limit UCL. Thus the plot 131 and the plot 132 represent different abnormality patterns, and accordingly the patent document 1 teaches that displaying the abnormalities in different manners according the pattern allows explicitly distinguishing the abnormalities on the control chart.
Semiconductor device production lines are highly automated and electronically controlled today, with little participation by a human in processing works. One process (equipment) and another are combined via an automatic conveyor, and upon completion of a wafer processing in a certain process the information to that effect is transmitted to a host computer, for executing a centralized control. Then the wafer is conveyed to the subsequent process.
For the purpose of controlling the processing performance of the wafer and the electrical characteristics, the thin films thickness, the interconnects line widths, widths of the trenches, number of micro-defects, electrical characteristics and so forth formed on the wafer are acquired through measurement of the wafer. The wafers to be measured include those on which the devices are actually being processed and a monitoring wafer employed exclusively for the measurement. The data thus acquired is often transmitted to the host computer upon completion of the processing work (including the measurement) of the respective processes.
Various data representing the status of the equipments employed for the wafer processing is also transmitted to the host computer. Such data may be transmitted upon completion of the process, or any time irrespective of the timing of finishing the process.
In the electronically controlled and automated semiconductor device production line, graph drawing of the control data and abnormality decision are also automated. In such a production line, the graphs are not made up by human handworks, but the host computer contains thousands to hundreds of thousand types of control graphs, on which more than a hundred thousand plots are marked per day, for monitoring the production process. The host computer also contains programs that enable retrieving only the abnormality decision information, thereby providing a highly efficient control system.
Here, various rules have conventionally been employed for deciding the abnormality in the system based on the control charts. For example, JIS specification (JIS Z9021) stipulates eight types of abnormality decision rules as a guideline. The abnormalities specified in those rules generally become visible when the process result has some abnormal tendency. In practical operations, not all of those rules are applied, but one of the rules is selected in consideration of fluctuation inherent to the process. Such procedure is expressly provided in JIS Z9021.
Referring to graphs shown in FIGS. 6 to 13, the eight patterns of abnormality decision rules will be reviewed. In all the graphs, a solid triangle (▴) represents the plot decided to be abnormal. The abnormality decision rules corresponding to the respective graphs are as follow:
FIG. 6: One plot exceeding the control limit
FIG. 7: Nine consecutive plots on either side of the centerline
FIG. 8: Six consecutive plots consecutively increasing or decreasing
FIG. 9: Fourteen plots alternately increasing and decreasing
FIG. 10: Two plots exceeding 2σ out of three consecutive plots, where σ is the standard deviation
FIG. 11: Four plots exceeding 1σ out of five consecutive plots
FIG. 12: Fifteen consecutive plots within 1σ
FIG. 13: Eight consecutively plots exceeding 1σ
In the case where the control characteristic values are in accordance with the normal distribution and the alignment of the plots is not biased, the probability that a type-1 error occurs from the decision rule relevant to FIG. 6 is 0.27%. With respect to the rules relevant to FIGS. 7 to 13 also, the probability that the type-1 error occurs is approximately 0.3%. The type-1 error herein means, as shown in FIG. 14, an error of deciding as abnormal despite actually being normal. On the other hand, an error of deciding as normal despite actually being abnormal is referred to as a type-2 error.
In the electronically controlled semiconductor device production line, it is not unusual that more than a hundred thousand plots are marked per day on thousands to hundreds of thousand types of control graphs. Accordingly, based on the rule relevant to FIG. 6, approximately 270 (100,000×0.27%) cases or more are decided to be abnormal each day, despite actually being normal. In addition, based on all the rules relevant to FIGS. 6 to 13, more than two thousand cases are decided to be abnormal though they are not. Some control characteristic values present a gradual increase or gradual decrease, even under a normal status. When the rule relevant to FIG. 7 or FIG. 8 is applied to such characteristic values, the probability that the type-1 error occurs becomes far much greater than 0.3%.
When an alarm is issued to announce that the abnormality decision has been made, the process is suspended by turning off the equipments and detaining the products, for performing inspection. Therefore, issuing more than two thousand alarms each day based on the type-1 error incurs significant waste of time and resources, since actually the alarms do not correspond to any abnormality.
Accordingly, it is essential to distinguish the alarms originating from real abnormalities. The alarms based on the type-1 error should be minimized to a lowest possible level, thus to permit only the alarm based on the real abnormality to be issued. That is why the abnormality decision rules are appropriately selected in consideration of the fluctuation inherent to each process, and focusing on abnormalities that have to be particularly sensitively detected.